fix: 🐞 temporarly ignore mypy errors for Literal/typer

Signed-off-by: Onuralp SEZER <[email protected]>

maestro/trainer/models/florence_2/entrypoint.py

@@ -72,15 +72,15 @@ def train(
     optimizer: Annotated[
         str,
         typer.Option("--optimizer", help="Optimizer to use for training"),
-    ] = "adamw",  # type: ignore
+    ] = "adamw",
     lr: Annotated[
         float,
         typer.Option("--lr", help="Learning rate for the optimizer"),
     ] = 1e-5,
     lr_scheduler: Annotated[
         str,
         typer.Option("--lr_scheduler", help="Learning rate scheduler"),
-    ] = "linear",  # type: ignore
+    ] = "linear",
     batch_size: Annotated[
         int,
         typer.Option("--batch_size", help="Batch size for training"),
@@ -112,15 +112,15 @@ def train(
     bias: Annotated[
         str,
         typer.Option("--bias", help="Which bias to train"),
-    ] = "none",  # type: ignore
+    ] = "none",
     use_rslora: Annotated[
         bool,
         typer.Option("--use_rslora/--no_use_rslora", help="Whether to use RSLoRA"),
     ] = True,
     init_lora_weights: Annotated[
         str,
         typer.Option("--init_lora_weights", help="How to initialize LoRA weights"),
-    ] = "gaussian",  # type: ignore
+    ] = "gaussian",
     output_dir: Annotated[
         str,
         typer.Option("--output_dir", help="Directory to save output files"),
@@ -138,19 +138,19 @@ def train(
         device=torch.device(device),
         cache_dir=cache_dir,
         epochs=epochs,
-        optimizer=optimizer,
+        optimizer=optimizer,  # type: ignore
         lr=lr,
-        lr_scheduler=lr_scheduler,
+        lr_scheduler=lr_scheduler,  # type: ignore
         batch_size=batch_size,
         val_batch_size=val_batch_size,
         num_workers=num_workers,
         val_num_workers=val_num_workers,
         lora_r=lora_r,
         lora_alpha=lora_alpha,
         lora_dropout=lora_dropout,
-        bias=bias,
+        bias=bias,  # type: ignore
         use_rslora=use_rslora,
-        init_lora_weights=init_lora_weights,
+        init_lora_weights=init_lora_weights,  # type: ignore
         output_dir=output_dir,
         metrics=metric_objects,
     )